Abstract
This work examines two methods for evolving
dimensionally correct equations on the basis of data.
It is demonstrated that the use of units of measurement
aids in evolving equations that are amenable to
interpretation by domain specialists. One method uses a
strong typing approach that implements a declarative
bias towards correct equations, the other method uses a
coercion mechanism in order to implement a preferential
bias towards the same objective. Four experiments using
real-world, unsolved scientific problems were performed
in order to examine the differences between the
approaches and to judge the worth of the induction
methods. Not only does the coercion approach perform
significantly better on two out of the four problems
when compared to the strongly typed approach, but it
also regularizes the expressions it induces, resulting
in a more reliable search process. A trade-off between
type correctness and ability to solve the problem is
identified. Due to the preferential bias implemented in
the coercion approach, this trade-off does not lead to
sub-optimal performance. No evidence is found that the
reduction of the search space achieved through
declarative bias helps in finding better solutions
faster. In fact, for the class of scientific discovery
problems the opposite seems to be the case.
Users
Please
log in to take part in the discussion (add own reviews or comments).